Dario, Regarding:
>This is especially concerning, as it means that accepting calls will completely stall when a long running call (e.g. retrieving state.json) is running. How does it help a client when it gets an early accepted response versus when accepting of calls is stalled i.e., queued up on the master actor? The client does not need to wait for a response before pipelining its next request to the master anyway. In your tests, do you send the next REVIVE call only upon receiving the response to the current call? That might explain the behavior you are seeing. -anand On Sun, Oct 16, 2016 at 11:58 AM, tommy xiao <xia...@gmail.com> wrote: > interesting this topic. > > 2016-10-17 2:51 GMT+08:00 Dario Rexin <dre...@apple.com>: > >> Hi Anand, >> >> I tested with current HEAD. After I saw low throughput on our own HTTP >> API client, I wrote a small server that sends out fake events and accepts >> calls and our client was able to send a lot more calls to that server. I >> also wrote a small tool that simply sends as many calls to Mesos as >> possible without handling any events and get similar results there.I also >> observe extremely high CPU usage. While my sending tool is using ~10% CPU, >> Mesos runs on ~185%. The calls I send for testing are all REVIVE and I >> don’t have any agents connected, so there should be essentially nothing >> happening. One reason I could think of for the reduced throughput is that >> all calls are processed in the master process, before it sends back an >> ACCEPTED, leading to effectively single threaded processing of HTTP calls, >> interleaved with all other calls that are sent to the master process. >> Libprocess however just forwards the messages to the master process and >> then immediately returns ACCEPTED. It also handles all connections in >> separate processes, whereas HTTP calls are effectively all handled by the >> master process.This is especially concerning, as it means that accepting >> calls will completely stall when a long running call (e.g. retrieving >> state.json) is running. >> >> Thanks, >> Dario >> >> On Oct 16, 2016, at 11:01 AM, Anand Mazumdar <an...@apache.org> wrote: >> >> Dario, >> >> Thanks for reporting this. Did you test this with 1.0 or the recent HEAD? >> We had done performance testing prior to 1.0rc1 and had not found any >> substantial discrepancy on the call ingestion path. Hence, we had focussed >> on fixing the performance issues around writing events on the stream in >> MESOS-5222 <https://issues.apache.org/jira/browse/MESOS-5222> and >> MESOS-5457 <https://issues.apache.org/jira/browse/MESOS-5457>. >> >> The numbers in the benchmark test pointed by Haosdent (v0 vs v1) differ >> due to the slowness of the client (scheduler library) in processing the >> status update events. We should add another benchmark that measures just >> the time taken by the master to write the events. I would file an issue >> shortly to address this. >> >> Do you mind filing an issue with more details on your test setup? >> >> -anand >> >> On Sun, Oct 16, 2016 at 12:05 AM, Dario Rexin <dre...@apple.com> wrote: >> >>> Hi haosdent, >>> >>> thanks for the pointer! Your results show exactly what I’m experiencing. >>> I think especially for bigger clusters this could be very problematic. It >>> would be great to get some input from the folks working on the HTTP API, >>> especially Anand. >>> >>> Thanks, >>> Dario >>> >>> On Oct 16, 2016, at 12:01 AM, haosdent <haosd...@gmail.com> wrote: >>> >>> Hmm, this is an interesting topic. @anandmazumdar create a benchmark >>> test case to compare v1 and v0 APIs before. You could run it via >>> >>> ``` >>> ./bin/mesos-tests.sh --benchmark --gtest_filter="*SchedulerReco >>> ncileTasks_BENCHMARK_Test*" >>> ``` >>> >>> Here is the result that run it in my machine. >>> >>> ``` >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/0 >>> Reconciling 1000 tasks took 386.451108ms using the scheduler library >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/0 (479 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/1 >>> Reconciling 10000 tasks took 3.389258444secs using the scheduler library >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/1 (3435 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/2 >>> Reconciling 50000 tasks took 16.624603964secs using the scheduler library >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/2 (16737 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/3 >>> Reconciling 100000 tasks took 33.134018718secs using the scheduler >>> library >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerLibrary/3 (33333 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/0 >>> Reconciling 1000 tasks took 24.212092ms using the scheduler driver >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/0 (89 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/1 >>> Reconciling 10000 tasks took 316.115078ms using the scheduler driver >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/1 (385 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/2 >>> Reconciling 50000 tasks took 1.239050154secs using the scheduler driver >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/2 (1379 ms) >>> [ RUN ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/3 >>> Reconciling 100000 tasks took 2.38445672secs using the scheduler driver >>> [ OK ] Tasks/SchedulerReconcileTasks_ >>> BENCHMARK_Test.SchedulerDriver/3 (2711 ms) >>> ``` >>> >>> *SchedulerLibrary* is the HTTP API, *SchedulerDriver* is the old way >>> based on libmesos.so. >>> >>> On Sun, Oct 16, 2016 at 2:41 PM, Dario Rexin <dre...@apple.com> wrote: >>> >>>> Hi all, >>>> >>>> I recently did some performance testing on the v1 scheduler API and >>>> found that throughput is around 10x lower than for the v0 API. Using 1 >>>> connection, I don’t get a lot more than 1,500 calls per second, where the >>>> v0 API can do ~15,000. If I use multiple connections, throughput maxes out >>>> at 3 connections and ~2,500 calls / s. If I add any more connections, the >>>> throughput per connection drops and the total throughput stays around >>>> ~2,500 calls / s. Has anyone done performance testing on the v1 API before? >>>> It seems a little strange to me, that it’s so much slower, given that the >>>> v0 API also uses HTTP (well, more or less). I would be thankful for any >>>> comments and experience reports of other users. >>>> >>>> Thanks, >>>> Dario >>>> >>>> >>> >>> >>> -- >>> Best Regards, >>> Haosdent Huang >>> >>> >>> >> >> > > > -- > Deshi Xiao > Twitter: xds2000 > E-mail: xiaods(AT)gmail.com > -- Anand Mazumdar
